Multilingual audio and video solutions.
Multilingual video and audio solutions to target international markets
YouTube has introduced a remarkable feature called multilingual audio, allowing users to watch videos in their native language. Initially, this feature was made available to a select group of creators, including the well-known YouTube personality, MrBeast. For viewers, this multilingual audio function enables them to enjoy dubbed videos in their preferred language, providing them with the opportunity to explore a wider range of content that they might have otherwise missed.
Dubbing involves replacing the original audio of a video with a recording in a different language. This additional audio is not part of the original video but is added during the post-production phase. Subtitles, on the other hand, consist of transcriptions of the audio, appearing on the screen for viewers to read. Unlike subtitles, dubbing completely replaces the original audio with the target language, ensuring that viewers remain engaged throughout the video.
YouTube’s dubbing function empowers creators to incorporate audio files in multiple languages, which users can select through the Settings icon. The concept behind this feature is simple: one video, multiple languages. This makes it more convenient and practical for creators to cater to a broader audience.
By enabling creators to add dubbing to their new and existing videos, YouTube aims to help them reach an international audience. The company developed the technology to support multilingual audio tracks in-house, but creators will need to collaborate with multilingual audio solution providers to create their own audio tracks. After uploading their videos, users will be able to choose a different audio track from the same menu where they adjust other settings like subtitles or audio quality. The selection of additional languages to support is at the discretion of the creator.
In initial tests conducted by YouTube with a small group of creators, the multilingual audio function was utilized for over 3,500 videos in more than 40 languages. According to YouTube, more than 15% of the viewing time for dubbed videos came from viewers who watched them in a different language than the original recording.
Initially, this feature will only support long-form content on YouTube, but the company is already experimenting with implementing it for short films. This launch will grant thousands of creators access to the new feature beyond the initial test group. Furthermore, the option to adjust a video’s audio track will be rolled out globally across YouTube, available on desktops, mobile phones, tablets, and TVs.
One of the prominent creators who participated in the initial test group was MrBeast (Jimmy Donaldson), known for having 130 million subscribers worldwide. He dubbed his 11 most popular videos into 11 different languages to attract a more diverse international audience to his channel. In an interview with YouTube’s Creator Insider site, Jimmy Donaldson emphasized the value of this feature, highlighting that uploading multilingual audio tracks is easier compared to managing and maintaining separate foreign-language channels.
Eligible creators who gain access to this feature will receive invitations to participate and utilize the new option in Creator Studio.
YouTube videos are already fairly accessible, but there is always room for improvement. The dubbing function addresses several gaps, leading to significant improvements for the following reasons:
Typically, people watch YouTube videos during their leisure time. While subtitles are beneficial for individuals who are deaf or hard of hearing, they require viewers to concentrate on both the words on the screen and the video itself. This can make the viewing experience less enjoyable, and multitasking may not be suitable for everyone.
By watching a video in their native language, viewers can avoid the need to read subtitles. It also enhances immersion as the audio captures cultural nuances and expressions that may be missed in subtitles alone.
The more videos that are dubbed, the greater the audience they can attract. YouTube automatically displays dubbed videos in the viewer’s
If you’re a creator, keep in mind that when dubbing your videos, a good strategy is to start with the world’s most popular languages and gradually add more. Add dubbing to your list of factors to consider when launching a YouTube channel.
People with reading difficulties such as dyslexia may find it difficult to read or follow subtitles. Conversely, dubbed videos are accessible to everyone. According to the International Dyslexia Association, around 15-20% of the world’s population suffers from some form of dyslexia. Catering to this particular audience can open up a new world to them that they might otherwise have been excluded from.
YouTube becomes more inclusive As the world becomes more inclusive, entertainment platforms no longer have an excuse not to appeal to as wide an audience as possible. YouTube is playing its part by widening the audience to which creators can appeal thanks to its audio dubbing function.
AI has drastically altered the way people go about their daily lives. Voice recognition has simplified activities like taking notes, typing documents, and more. Its speed and efficiency are what makes it so popular. With the progress made in AI, many voice recognition applications have been created. Google, Alexa, and Siri are a few examples of virtual assistants that use voice recognition software to communicate with users. Additionally, text–to–speech, speech–to–text, and text–to–text have been widely adopted in various applications.
Creating human-level speech is essential for Artificial Intelligence (AI), especially when it comes to chatbots. Recent advances in deep learning have drastically improved the quality of synthesized speech produced by neural-based Text-to-Speech (TTS) systems. However, most of the data used for training these systems have been limited to recordings from controlled environments, such as reading aloud or performing a script. Human beings on the other hand, can speak spontaneously with varied prosodies that convey paralinguistic information, such as subtle emotions. This ability is acquired from being exposed to a long duration of real-world speech.
Here are 3 new ways that will dramatically improve text to speech.
Researchers at Carnegie Mellon University have developed an artificial intelligence (AI) system that can be trained to generate text–to–speech with a wide range of voices. To do this, they analyzed actual speech taken from YouTube videos and podcasts. By using existing recordings, they could simplify the environment and focus on text–to–speech. They hope that this will replicate the success of large language models like GPT–3.
Using a limited amount of resources, these systems can be tailored to particular speaker qualities or recording conditions. This paper examines the new challenges that arise when training TTS systems on actual speech, such as increased prosodic variance and background noise that are not found in speech recorded in controlled environments. The authors show that the use of mel–spectrogram–based autoregressive algorithms cannot reproduce accurate text–audio alignment when applied to real–world speech, resulting in distorted speech. The failure of inference alignment is attributed to the errors that accumulate in the decoding process, as they also demonstrate that precise alignments can be learned during the training phase.
Researchers found that replacing the mel–spectrogram with a learned discrete codebook could solve the problem. This is due to the fact that discrete representations are more resistant to input noise. However, their research showed that a single codebook still produced distorted speech even when the codebook was increased in size. It is believed that there are too many prosody patterns in spontaneous speech for a single codebook to capture. Therefore, multiple codebooks were used to create architectures for multi–code sampling and monotonic alignment. A pure silence audio prompt was used during the inference process to ensure that the model produced clear speech despite being trained on a noisy corpus.
In this paper, the authors present their new technology MQTTS (multi–codebook vector quantized TTS). To understand its potential for real–world voice synthesis, they compare mel–spectrogram–based systems in Section 5 and carry out an ablation analysis. They then compare MQTTS to non–autoregressive models, finding that it produces better intelligibility and speaker transferability. Additionally, MQTTS has greater prosody variety and naturalness, though non–autoregressive models have faster computing speed and higher resilience. Furthermore, MQTTS may achieve a lower signal–to–noise ratio with a clean, quiet cue (SNR). The authors make their source code available on GitHub for public use.
The highly successful T5 (Text-To-Text Transfer Transformer) has been the inspiration for the SpeechT5 framework, a unified-model which uses encoder-decoder pre-training for self-supervised learning of speech/text representation. The SpeechT5 model has now been added to the Hugging Face Transformers toolkit, an open-source library with easy access to the latest machine learning models.
SpeechT5 utilizes a conventional encoder–decoder design to develop combined contextual representations for both voice and text. It features three distinct speech models: text–to–speech (for creating audio from nothing), speech–to–text (for automated speech recognition), and speech–to–speech (for carrying out speech augmentation or changing between voices).
The core concept of SpeechT5 is to prepare a single model by combining text–to–speech, speech–to–text, text–to–text, and speech–to–speech data. This encourages the model to learn from both speech and written text. The base of SpeechT5 is a standard Transformer encoder–decoder structure, which can perform sequential transformations with hidden representations, like any other Transformer. Pre–nets and post–nets are added to make the same Transformer suitable for text and audio. The pre–nets convert the input of text or speech into the Transformer‘s hidden representations, while the post–nets convert the Transformer‘s outputs into text or speech. To train the model for multiple languages, the team supplies it with text/speech data as input and produces the corresponding output as text/speech.
SpeechT5 stands out from other models as it allows for multiple activities to be carried out with one architecture, simply by adapting the pre–nets and post–nets. The model has been fine–tuned to tackle a variety of tasks, and studies have shown that it outshines all baseline models in a number of spoken language processing tasks. To improve the model even further, scientists plan to pre–train SpeechT5 with a larger model and more unlabeled data. Additionally, they are exploring ways to use the framework to handle tasks involving spoken language processing in multiple languages.
Microsoft has developed a revolutionary language model for text–to–speech synthesis (TTS) known as VALL–E. The AI utilizes audio codec codes as intermediate representations and is capable of replicating someone‘s voice with only three seconds of audio input. VALL–E is a neural codec language model which tokenizes speech, and then uses algorithms to generate waveforms which sound like the speaker, even replicating their unique timbre and emotional tone. As stated in the research paper, VALL–E can produce high–quality personalized speech with just a three–second sample of the speaker‘s voice, without the need for additional structural engineering, pre–made acoustic features, or fine–tuning. It also supports contextual learning and prompt–based zero–shot TTS approaches. Demonstration audio clips are provided in the research paper, with one sample being a three–second prompt that VALL–E must replicate. To compare, another sample is a previously–recorded phrase by the same speaker (the “ground truth“), while the “baseline“ sample is a typical text–to–speech synthesis example, and the “VALL–E“ sample is the output of the VALL–E model.
ChatGPT is a chatbot developed by OpenAI. It is based on instructGPT: it has been trained to respond to instructions or “prompts” written by users.
ChatGPT shows an impressive ability to provide detailed, consistent and relevant answers. It appears to be particularly good at natural language processing (NLP) tasks such as summarising, answering questions, generating speech and machine translation.
However, as a very new system, ChatGPT still needs to be scientifically evaluated to compare its natural language processing performance with previous work. Read more
Hangeul, as the Korean script is known, was formulated in a document called Hunmin Jeongeum by King Sejong in 1443, the 25th year of his reign. It was then tested and improved for another three years. Originally, 28 letters were created, of which only 24 are used today.
Hangeul is made up of consonants and vowels. It is a phonetic system, but it is based on the formation of syllabic units. The syllable consists of a first sound, an intermediate sound and a last consonant.
What makes it unique is the fact that the basic consonants were created by reproducing the human organs of pronunciation, mimicking the shapes of the organ of articulation as they are pronounced. Other letters were developed on the basis of these basic characters, taking into account similarities in sounds and stress levels and adding features to the basic characters.
The whole system was developed using the basic consonants and the three vowels and adding strokes to them, making the letters simple to learn. In modern times, Hangeul has been easily combined with digital technologies, making it easier to input.
Korean belongs to the Ural-Altaic group of languages, which extends closely across Mongolia and Central Asia to Turkey. The ancestors of modern Koreans are thought to have brought the language to the Korean peninsula from their home in Central Asia. Korean is very similar to Japanese in terms of grammar and sentence structure. Today, Korean is spoken by about 70 million people.
Understanding Korean and Korean culture for successful audio / video translations
It is extremely difficult for most foreigners to master spoken Korean. In contrast, the written Korean language, thanks to the foresight of King Sejong, is relatively easy and can be learned in a few hours of study.
Did you know that, contrary to legend, the Korean language is not related to Japanese? In fact, it is a member of the Altaic language family, which also includes Turkish, Mongolian and Finnish. Or that there are over 80 dialects spoken in Korea? Here are some specific aspects that make the language unique. Information to better understand this society and to succeed with audio / video translations into Korean.
The biggest influences on the Korean language come from English and Chinese, respectively. While the South Korean language reflects these influences, North Korea strives to keep the language free of loanwords. This means that there may be different ways of saying things in the North and South.
But don’t worry, the differences are not so great that you can’t learn and use Korean. It is still the official language of both countries.
Many people look at the Korean script and think that it is based on pictures or ideograms, like Chinese and Japanese characters. However, what you see when you look at one of their characters is not a picture; it is a combination of sounds, and the letters that make up a syllable.
There are also the more advanced nuances that come from the honorific system. This system determines how you address someone, depending on your relationship with that person. There are different levels of language, each corresponding to a different level of respect. To find out more about Korean language levels, click here.
For example, you will use completely different words when addressing a relative than when speaking to your employer. This system can be quite complex.
In English, words change depending on whether the subject is singular or plural. For example, if there is a distinction between one book and four books, we add an “s” to the end of the word. In Korean, not all words take a different plural form to indicate more than one person, place or thing. Most of the time, it is the context of the sentence that indicates whether the subject is singular or plural.
In much of everyday Korean language, the subject and object can be removed from a sentence. The person you are addressing will understand because of the context.
In English, the basic sentence consists of the subject, verb and object. In Korean, the basic word order is subject, object, verb.
– S-V-O – He feeds the dog
– S-O-V – He feeds the dog
In Korean, you can often drop the subject and object as long as the context is present. In almost all cases, the verb is the most important part.
The Korean language is interesting in that it has been heavily influenced by Chinese throughout its history. In fact, a large part of the Korean vocabulary consists of words of Chinese origin.
This is because for centuries Korea was a vassal state of China. As a result, the two cultures had a profound impact on each other. Even today, many Koreans use Chinese characters in their writing.
However, the Korean language has also been influenced by other languages, such as English and Japanese. As Korea continues to modernise and globalisation takes hold, it will be interesting to see how the Korean language develops in the future.
The Korean language is unique in several ways. First, it is what linguists call an “isolated language”, i.e. it is not closely related to any other known language. This is in contrast to languages such as English, French and Spanish, which all belong to the Indo-European language family.
The Korean language is also believed to have remained relatively unchanged for over 2,000 years. This stability is unusual for languages, which generally evolve and change over time.
In addition, the Korean alphabet (known as Hangul) is particularly distinctive. It was invented in the 15th century by King Sejong the Great and consists of 24 letters (14 consonants and 10 vowels). Finally, Korea has a complex system of honorific formulas used to show respect to elders or superiors. All these features make the Korean language unique and interesting.
Korean has two different numbering systems: native Korean and Chinese-Korean. Korean numbers are used to count things like people and animals, while Chinese-Korean numbers are used to count everything else, like money and days of the week.
The two numbering systems are completely different, which can be confusing for Korean learners.
For example, the number “eleven” is “ship” in native Korean, but “pal” in Chinese-Korean. In addition, some words can be counted using either system. For example, the word “person” can be counted as “one person” or “ten people”. Therefore, it is important to learn both numbering systems in order to count correctly in Korean.
Before that, Koreans used Chinese characters to write. In 1443, King Sejong the Great commissioned a team of scholars to create a new alphabet for the Korean language.
They developed a 28-letter system called “hangul”. This new alphabet enabled people from all social classes to learn to read and write. Today, Hangul is still used in North and South Korea. It is considered one of the most logical and easy to learn writing systems in the world!
The Korean government has decided to create a public holiday called “Hangeul Day”. Hangeul Day is celebrated on 9 October, the day the Korean alphabet was created in 1446. On this day, Koreans around the world celebrate by learning more about the Korean language and culture. If you want to learn more about the Korean language, Hangeul Day is a good time to start!
The Korean alphabet, known as Hangul, is often considered the “best alphabet in the world”. And it’s not hard to see why. Hangul was specifically designed to be easy to learn and use, and is considered one of the most scientific writing systems available.
The alphabet consists of just 24 letters, which can be combined to form thousands of different words. And unlike many other writing systems, there is no ambiguity in the way the letters are pronounced. In addition, the alphabet is very efficient and uses far fewer symbols than most other languages. As a result, it is estimated that the average Korean can read and write twice as fast as an English speaker.
In Korean, there is no word that corresponds to the English “I”. Instead, the pronoun “우리(uri)”, which means “we”, is used to refer to oneself. This usage reflects the collectivist nature of Korean society. Collectivism is a social philosophy that emphasises the needs of the group rather than the individual.
In collectivist cultures, people are expected to work together for the common good. Individual success is often secondary to the success of the group. The collectivist orientation of Koreans is evident in their family relationships, their interactions with friends, and their attitude towards work. In many ways, collectivism is the cornerstone of Korean society.
If you’re thinking of learning Korean, be warned: consonants may give you a hard time. Unlike English, which has a relatively simple consonant system, Korean has a much more complex system. There are 19 different consonants in Korean, each with a unique sound.
What is more, these consonants can be combined to form even more complex sounds. Therefore, mastering Korean pronunciation can be a challenge for even the most experienced learners. But don’t let this discourage you! With a little practice, you’ll be speaking like a native speaker in no time.
The Korean peninsula is a unique place because it is home to multiple dialects of the Korean language. Although they are separated by only a few hundred kilometres, these dialects can be very different from each other. This is one of the many interesting facts about the Korean language!
For example, the people of Jeju Island have their own way of speaking, which includes several words borrowed from the Jeju dialect of the Chinese language. In addition, there are also differences between the standard Korean spoken in North and South Korea. These differences date back to the division of the peninsula into two separate countries after the Second World War.
As a result, the Korean language has developed into a rich and diverse mosaic, with many dialects and variations.
Over the past decade, the popularity of Korean pop music (K-Pop) and Korean TV shows has increased significantly worldwide. This has led to a growing interest in learning the Korean language. Indeed, many people who would never have considered studying Korean are now enrolling in courses or taking online courses.
There are several reasons for this trend. Firstly, Korean K-Pop and soap operas are extremely popular and widely available. They are an enjoyable way to learn about Korean culture and acquire basic language skills. Secondly, the rise of social media means that it is easier than ever to connect with Korean speakers and learn directly from them. Finally, more and more people are realising that knowledge of Korean can be a valuable asset in an increasingly globalised world.
Korea has been occupied or influenced by other countries for much of its history. As a result, the Korean language has borrowed heavily from other languages, especially Chinese. According to one estimate, almost 60% of Korean vocabulary consists of words borrowed from Chinese.
However, Korean has also borrowed words from Japanese, Mongolian, English and other languages. These borrowings often reflect the technological or economic dominance of the borrowing culture at the time.
For example, many Korean words for modern technological devices are borrowed from English, while older words for traditional Korean objects are more often of Chinese origin. As Korean evolves, it is likely that more loanwords will appear in the language.
Korean has no grammatical gender, which means that there are no masculine or feminine forms for nouns. This can be a bit shocking for learners of Korean who are used to English, where almost all nouns have a gender.
For example, the word for ‘book’ is 책 (chae), which is neuter, while the word for ‘bird’ is 새 (sae), which is feminine. The lack of grammatical gender in Korean means that there are no pronouns like “he” or “she”, and all verbs and adjectives are conjugated the same, regardless of the gender of the subject.
Although it takes some getting used to, not having to worry about gender can, in some ways, make learning Korean easier. So don’t worry if you can’t tell if a Korean noun is masculine or feminine – it’s likely to have no gender at all!
Here are a few things we hope will help you develop your presence in Korea, or adapt your audio / video content. Do not hesitate to consult LenseUp for all your audio / video projects.
Google has announced a new project to build an AI model that can support the world’s 1,000 most spoken languages. The company has presented an AI model that has been trained in over 400 languages, which it describes as the “largest language coverage seen in a speech model today.” This new project emphasizes Google’s commitment to language and AI.
Google has announced the development of a “giant” AI language model that can handle more than 1,000 global languages. The company has been working on the project for a while now, and it’s already made some progress. With the help of machine learning, Google has been able to translate between languages with “zero human intervention.” Now, with the new AI language model, the company is hoping to take things to the next level. The goal is to make it easier for people to communicate with each other, regardless of the language they speak. Read more
Computers are already able to play chess games and they became unbeatable opponents; we let them read our texts and they started to write. They also learned to paint and retouch photographs. Did anyone doubt that artificial intelligence would be able to do the same with speeches and music?
Google’s research division has presented AudioLM, a framework for generating high-quality audio that remains consistent over the long term. To do this, it starts with a recording of just a few seconds in length, and is able to prolong it in a natural and coherent way. What is remarkable is that it achieves this without being trained with previous transcriptions or annotations even though the generated speech is syntactically and semantically correct Moreover, it maintains the identity and prosody of the speaker to such an extent that the listener is unable to discern which part of the audio is original and which has been generated by an artificial intelligence.
The examples of this artificial intelligence are striking. Not only is it able to replicate articulation, pitch, timbre and intensity, but it is able to input the sound of the speaker’s breathing and form meaningful sentences. If it does not start from a studio audio, but from one with background noise, AudioLM replicates it to give it continuity. More samples can be heard on the AudioLM website. Read more
OpenAI has introduced a new automatic speech recognition (ASR) system called Whisper as an open-source software kit on GitHub. Whisper’s AI can transcribe conversations in multiple languages and translate them into English, and the GPT-3 teams claim that Whisper’s training makes it easier to distinguish voices in noisy environments and understand heavy accents and technical language.
Automatic speech recognition, often called ASR, turns spoken language into text. Speech-to-text software that automatically converts your voice into written language.
This technology has many applications, including dictation and visual voice messaging software. Read more
In the past, if someone wanted to learn, they would have assist a presential course, to read abou in books, or research papers. However, the internet and smart devices have made learning available to everyone. Now, all you need is a click away because eLearning has harnessed the full power of the latest technologies. Language services have also made it possible to exchange information at a global level.
The pandemic has both had good and bad effects on eLearning. It has improved the existing eLearning movement and has made organizations that were on the sidelines use this opportunity to train their employees.
If you want to deliver a video to an international audience, you need to make sure that it is 100% understandable to your audience. Taking into account the cultural norms, beliefs and values of a culture is crucial to making a good impression on a target audience.
90% of consumers believe that product videos are useful in the purchase decision process. And today, 82% of all content will be in video format. Read more
Hispanics have their own distinct culture and language, which has led to the development of a unique linguistic culture in the U.S. Companies that want to reach the Hispanic market need to adapt their content to this specific audience.
The U.S. Hispanic community is a rapidly growing demographic that currently makes up over 14% of the general population. This community has significant buying power and is also the largest minority group in the U.S. The Hispanic community is also relatively young, with 40% of the population falling into the millennial age range (born between 1981-1996). Just over a quarter of the U.S. population under the age of nine is Hispanic. According to the U.S. Census Bureau, the Hispanic population grew by 43 percent between 2000 and 2010, and is projected to grow by another 28 percent by 2025. Consequently, the Hispanic market is becoming increasingly important for companies that want to tap into this growing demographic. When localizing audio content or translating video into Spanish, it is important to keep in mind the variations in the Spanish language. Read more
Multilingual video and audio solutions to target international markets
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